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Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors
Kangfu Mei, Nithin Gopalakrishnan Nair, Vishal M. Patel
Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentime…
Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel
Generating photos satisfying multiple constraints find broad utility in the content creation industry. A key hurdle to accomplishing this task is the need for paired data consistin…
VIDM: Video Implicit Diffusion Models
Kangfu Mei, Vishal M. Patel
Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on…
SceneComposer: Any-Level Semantic Image Synthesis
Yu Zeng, Zhe Lin, Jianming Zhang +4
We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More s…
AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders
Wele Gedara Chaminda Bandara, Naman Patel, Ali Gholami +3
Masked Autoencoders (MAEs) learn generalizable representations for image, text, audio, video, etc., by reconstructing masked input data from tokens of the visible data. Current MAE…
Open-Set Automatic Target Recognition
Bardia Safaei, Vibashan VS, Celso M. de Melo +2
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are exten…
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images
Nithin Gopalakrishnan Nair, Rajeev Yasarla, Vishal M. Patel
In recent years, deep neural network-based restoration methods have achieved state-of-the-art results in various image deblurring tasks. However, one major drawback of deep learnin…
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
Although many long-range imaging systems are designed to support extended vision applications, a natural obstacle to their operation is degradation due to atmospheric turbulence. A…
T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Vishal M. Patel
Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using…
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation
Shao-Yuan Lo, Wei Wang, Jim Thomas +3
Monocular depth estimation (MDE) has attracted intense study due to its low cost and critical functions for robotic tasks such as localization, mapping and obstacle detection. Supe…
Deep Semantic Statistics Matching (D2SM) Denoising Network
Kangfu Mei, Vishal M. Patel, Rui Huang
The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learnin…
Learning to restore images degraded by atmospheric turbulence using uncertainty
Rajeev Yasarla, Vishal M. Patel
Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index o…
Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency
Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu +1
Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures. However, these tasks are challenging due t…
Orientation-guided Graph Convolutional Network for Bone Surface Segmentation
Aimon Rahman, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu +2
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the s…
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M Patel
Image synthesis under multi-modal priors is a useful and challenging task that has received increasing attention in recent years. A major challenge in using generative models to ac…
SAR Despeckling using a Denoising Diffusion Probabilistic Model
Malsha V. Perera, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara +1
Speckle is a multiplicative noise which affects all coherent imaging modalities including Synthetic Aperture Radar (SAR) images. The presence of speckle degrades the image quality…
SAR Despeckling Using Overcomplete Convolutional Networks
Malsha V. Perera, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu +1
Synthetic Aperture Radar (SAR) despeckling is an important problem in remote sensing as speckle degrades SAR images, affecting downstream tasks like detection and segmentation. Rec…
On Trace of PGD-Like Adversarial Attacks
Mo Zhou, Vishal M. Patel
Adversarial attacks pose safety and security concerns to deep learning applications, but their characteristics are under-explored. Yet largely imperceptible, a strong trace could h…
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN
Rajeev Yasarla, Vishwanath A. Sindagi, Vishal M. Patel
Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training. However, collecting paired data for weather…
Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
Wele Gedara Chaminda Bandara, Vishal M. Patel
Remote-sensing (RS) Change Detection (CD) aims to detect "changes of interest" from co-registered bi-temporal images. The performance of existing deep supervised CD methods is attr…
A comparison of different atmospheric turbulence simulation methods for image restoration
Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems by introducing blur and geometric distortions to the captured scene. This leads to…
Shape-guided Object Inpainting
Yu Zeng, Zhe Lin, Vishal M. Patel
Previous works on image inpainting mainly focus on inpainting background or partially missing objects, while the problem of inpainting an entire missing object remains unexplored.…
Thermal to Visible Image Synthesis under Atmospheric Turbulence
Kangfu Mei, Yiqun Mei, Vishal M. Patel
In many practical applications of long-range imaging such as biometrics and surveillance, thermal imagining modalities are often used to capture images in low-light and nighttime c…
Towards Online Domain Adaptive Object Detection
Vibashan VS, Poojan Oza, Vishal M. Patel
Existing object detection models assume both the training and test data are sampled from the same source domain. This assumption does not hold true when these detectors are deploye…